Retail stores have used different technologies to identify products, the most notable technology being bar codes. However, there are other technologies available, such as radio frequency identification (RFID). Although both bar codes and RFID may be used to identify products, RFID does not require direct contact or line-of-sight scanning.
FIG. 1 is a diagram of a conventional RFID system 100. For example, RFID system 100 may include a reader 105, a reader antenna 110, and a transponder 115 (sometimes referred to as a “tag”). Transponder 115 may include a microchip 120 (e.g., made of silicon) attached to a transponder antenna 125. Microchip 120 may be embedded in transponder 115 and may be as small as a half a millimeter square. Transponder 115 may include an identifier (e.g., a serial number) that is capable of being transmitted up to a distance of several meters. Some transponders (e.g., a passive RFID transponder) may not include an internal power source. Rather, a passive transponder may acquire power (e.g., through induction) from a radio signal 130 transmitted by reader 105. In such instances, transponder 115 may transmit its identifier in a radio signal 135 back to reader 105 based on radio signal 130.
RFID technology may be applied to almost any physical object (e.g., a human being, an animal, or a consumer product). Given the expansive nature of this technology, some companies have been motivated to provide certain standards. For example, EPCglobal is a working group that has developed industry standards for the Electronic Product Code (EPC) based on RFID technology. FIG. 2 is a diagram illustrating a conventional EPCglobal standard format for EPCs. As illustrated, an exemplary EPC 200 may include a header 205, an EPC manager number 210, an object class 215, and a serial number 220. Header 205 and EPC manager number 210 may be fields of EPC 200 that are assigned by EPCglobal, while object class 215 and serial number 220 may be fields of EPC 200 that are assigned by an EPC manager number owner (e.g., a company).
Header 205 may identify the structure (e.g., a length, a type, a version, and/or a generation) of EPC 200. EPC manager number 210 may identify an entity (e.g., a company) responsible for maintaining the subsequent fields (i.e., object class 215 and serial number 220). In other words, EPC manager number 210 may identify the company responsible for the product or thing associated with EPC 200. Object class 215 may identify a class of objects (e.g., a category or a type of thing, similar to a stock keeping unit (SKU)). Serial number 220 may identify an instance of the class (e.g., a specific object within the category).
EPC 200 may be based on different formats. For example, EPC 200 may be based on a 64-bit format or a 96-bit format. FIG. 3 is a diagram of a conventional 64-bit serialized global trade identification number (SGTIN) format 300. As illustrated, 64-bit SGTIN format 300 may include a variety of fields, such as a header 305, a filter value 310, a company prefix index 315, an item reference 320, and a serial number 325.
Header 305 may identify the structure of the EPC. For example, header 305 may indicate that the tag data is encoded according to 64-bit SGTIN format 300. Filter value 310 may be used for filtering and pre-selection of basic logistics types, such as items, cases, and pallets. Company prefix index 315 may be used as an index for table look-up (e.g., an offset into a company prefix index table) for a company prefix registered to EPCglobal. Item reference 320 may identify a category or a type of an item. Serial number 325 may indicate a particular item of item reference 320.
For description purposes only, FIG. 3 also illustrates a bit capacity 330 and a decimal capacity 335. Bit capacity 330 may indicate a number of bits associated with each field described above. As illustrated, header 305 may be 2 bits long, filter value 310 may be 3 bits long, company prefix index 315 may be 14 bits long, item reference 320 may be 20 bits long, and serial number 325 may be 25 bits long. Similarly, decimal capacity 335 may indicate a corresponding base-ten numeric capacity. Header 305 may contain a specific value where a decimal capacity is inapplicable (e.g., 10 (base-two) or 2 (base-ten)). Additionally, the decimal capacity for item reference 320 depends on a length of company prefix index 315. Hence, the decimal capacity for item reference 320 is illustrated as a range of decimal values.
FIG. 4 is a diagram of a conventional 96-bit SGTIN format 400. As illustrated, 96-bit SGTIN format 400 may include a variety of fields, such as a header 405, a filter value 410, a partition 415, a company prefix 420, an item reference 425, and a serial number 430.
Header 405 may indicate the structure of the EPC. For example, header 405 may indicate that tag data is encoded according to a 96-bit SGTIN format 400. Filter value 410 may be used for filtering and pre-selection of basic logistics types, such as items, cases, and pallets. Partition 415 may indicate how subsequent fields are divided so as to obtain the correct data for each. That is, partition 415 may indicate company prefix 420 and item reference 425 bit-structures. Company prefix 420 may include the company's European Article Numbering-Uniform Code Council (EAN.UCC) company prefix number. Item reference 425 may include the item's GTIN item reference number. Serial number 430 may include the item's unique serial number.
For description purposes only, FIG. 4 also illustrates a bit capacity 435 and a decimal capacity 440. Bit capacity 435 may indicate a number of bits associated with each field described above. As illustrated, header 405 may be 8 bits long, filter value 410 may be 3 bits long, partition 415 may be 3 bits long, company prefix 420 may be 20-40 bits long, item reference 425 may be 24-40 bits long, and serial number 430 may be 38 bits long. Similarly, decimal capacity 440 may indicate a corresponding base-ten numeric capacity. Header 405 may contain a specific value where a decimal capacity is inapplicable (e.g., 0011 0000 (base-two) or 48 (base-ten)). Additionally, the decimal value for company prefix 420 and item reference 425 may depend on the content of partition 415. Hence, the decimal capacity for company prefix 420 and item reference 425 are illustrated as ranges.
FIG. 5 is a diagram of a portion of a conventional EPC 96-bit SGTIN tag 500 in human-readable form. For example, filter value 410 may have a value of “3” that indicates the item is a single shipping/consumer trade item 505. Company prefix 420 may have a value of “0037000” that indicates the company as Company XYZ 510. Item reference 425 may have a value of “06542” that indicates the item is Paper Towels—15 Pack 515, and serial number 430 may have a value of “773346595” that provides a unique serial number for item 520.
While FIGS. 3-5 have illustrated a SGTIN scheme, other schemes, formats, industrial bar code standards and/or identifiers may be additionally and/or alternatively used (e.g., Serial Shipping Container Code (SSCC), Global Location Number (GLN), Global Individual Asset Identifier (GIAI), GS1 EAN.UCC 128, Transfer Syntax, Health Industry Bar Code (HIBC), Automotive Industry Action Group (AIAG), etc.). Additionally, or alternatively, while RFID has been described, other NFC may be used, such as Bluetooth, Wireless Universal Serial Bus (WUSB), Ultra Wide Band (UWB), etc.
Another area related to communications is data mining. Generally, data mining (sometimes referred to as data discovery or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. For example, the information may be used to discover hidden information to increase revenue, cut costs, make services more personalized, etc.
Data mining tools may be employed to predict future trends and behaviors that allow businesses to make proactive, knowledge-driven decisions. In some cases, a data mining tool may automatically answer business questions that traditionally were too time-consuming to resolve. In this regard, patterns or predictive information stemming from these data mining models provide businesses with an indispensable asset that otherwise would have been previously missed by alternate methods.
Thus, data mining may be defined as an automated extraction of predictive information from (large) databases. Consequently, there are two keywords, namely, automated and predictive. That is, data mining models may include algorithms that find patterns, relations, profiles, classifications, etc., in an automated fashion, which provide results that can be used to predict the future with some degree of certainty.
Nevertheless, there are some drawbacks to NFC and data mining products and applications. For example, data mining applications do not take advantage of an information flow from, for example, NFC systems, that may describe an environment or surroundings of a user and may add context.